scholarly journals FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

2018 ◽  
Vol 9 (1) ◽  
pp. 41 ◽  
Author(s):  
Guolai Jiang ◽  
Lei Yin ◽  
Guodong Liu ◽  
Weina Xi ◽  
Yongsheng Ou
Keyword(s):  
Rotation Angle ◽  
Low Cost ◽  
Complex Environments ◽  
Scan Matching ◽  
Laser Range ◽  
Localization And Mapping ◽  
Laser Range Finders ◽  
Feature Based ◽  
Scan Data ◽  
The Stability

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.

scholarly journals Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4604
Author(s):  
Fei Zhou ◽  
Limin Zhang ◽  
Chaolong Deng ◽  
Xinyue Fan
Keyword(s):  
Visual Slam ◽  
Complex Environments ◽  
Line Segments ◽  
Structure Information ◽  
Line Feature ◽  
Localization And Mapping ◽  
Feature Based ◽  
Line Features ◽  
Point Line ◽  
Local Adaptive Threshold

Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.

Accurate Localization for Making Maps to Mobile Robots Using Odometry and GPS Without Scan-Matching

2015 ◽  
Vol 27 (4) ◽  
pp. 410-418 ◽  
Author(s):  
Masashi Yokozuka ◽  
◽  
Osamu Matsumoto
Keyword(s):  
Mobile Robots ◽  
Low Cost ◽  
Bundle Adjustment ◽  
Scan Matching ◽  
Accurate Localization ◽  
Rtk Gps ◽  
Scan Data ◽  
Gps Accuracy ◽  
Manual Input ◽  
Gps Device

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/11.jpg"" width=""300"" /> Comparison of mapping results</div> This paper studies an accurate localization method to make maps for mobile robots using odometry and a global positioning system (GPS) without scan matching. We investigate requirements for GPS accuracy in map-making. To generate accurate maps, SLAM techniques such as scan matching are used to obtain accurate positions. Scan matching is unstable, however, in complex environments and has a high computation cost. To avoid these problems, we studied accurate localization without scan matching. Loop closing is an important property in generating consistent maps. Inconsistencies in maps prevent correct routes to destinations from being generated. Basically, our method adds scan data to a map along a trajectory given by odometry. Odometry accumulates errors due, e.g., to wheel slippage or wheel diameter variations. To remove this accumulated error, we used bundle adjustment, introducing two types of processing. The first is a simple manual input moving a robot to a same position at start and end. This is equal that a robot returns to a start position at end. The second process uses a GPS device to improve map accuracy. Results of experiments showed that an accurate map is generated by using wheel-encoder odometry and a low-cost GPS device. Results were evaluated using a real-time kinematic (RTK) GPS device whose accuracy is within a few centimeters. </span>

scholarly journals Correction methods for underwater scan data measured by general-purpose laser range finders

2015 ◽  
Vol 81 (831) ◽  
pp. 15-00412-15-00412 ◽  
Author(s):  
Tomofumi FUJIWARA ◽  
Tetsushi KAMEGAWA ◽  
Jun HASEGAWA ◽  
Akio GOFUKU
Keyword(s):  
General Purpose ◽  
Laser Range ◽  
Laser Range Finders ◽  
Scan Data

scholarly journals IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

2016 ◽  
Vol XLI-B5 ◽  
pp. 533-539
Author(s):  
H. A. Mohamed ◽  
A. M. Moussa ◽  
M. M. Elhabiby ◽  
N. El-Sheimy ◽  
Abu B. Sesay
Keyword(s):  
Real Time ◽  
Data Sets ◽  
Indoor Environments ◽  
Scan Matching ◽  
Laser Range ◽  
Time Consumption ◽  
Matching Process ◽  
Laser Range Finders ◽  
Transformation Parameters ◽  
Time Scan

The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

scholarly journals IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

2016 ◽  
Vol XLI-B5 ◽  
pp. 533-539 ◽  
Author(s):  
H. A. Mohamed ◽  
A. M. Moussa ◽  
M. M. Elhabiby ◽  
N. El-Sheimy ◽  
Abu B. Sesay
Keyword(s):  
Real Time ◽  
Data Sets ◽  
Indoor Environments ◽  
Scan Matching ◽  
Laser Range ◽  
Time Consumption ◽  
Matching Process ◽  
Laser Range Finders ◽  
Transformation Parameters ◽  
Time Scan

The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

scholarly journals A V-SLAM Guided and Portable System for Photogrammetric Applications

2021 ◽  
Vol 13 (12) ◽  
pp. 2351
Author(s):  
Alessandro Torresani ◽  
Fabio Menna ◽  
Roberto Battisti ◽  
Fabio Remondino
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Motion Blur ◽  
Guidance System ◽  
Prototype System ◽  
Active Sensors ◽  
Localization And Mapping ◽  
Acquisition Speed ◽  
Mobile Mapping System ◽  
Slam Algorithm

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

scholarly journals Exploring the Structural Competition between the Black and the Yellow Phase of CsPbI3

Nanomaterials ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1282
Author(s):  
Ioannis Deretzis ◽  
Corrado Bongiorno ◽  
Giovanni Mannino ◽  
Emanuele Smecca ◽  
Salvatore Sanzaro ◽  
...  
Keyword(s):  
Perovskite Phase ◽  
Low Cost ◽  
Δ Phase ◽  
Nanocrystalline Structures ◽  
Halide Perovskites ◽  
The Stability ◽  
Structural Competition ◽  
Lead Halide ◽  
Main Candidate ◽  
Spontaneous Phase

The realization of stable inorganic perovskites is crucial to enable low-cost solution-processed photovoltaics. However, the main candidate material, CsPbI3, suffers from a spontaneous phase transition at room temperature towards a photo-inactive orthorhombic δ-phase (yellow phase). Here we used theoretical and experimental methods to study the structural and electronic features that determine the stability of the CsPbI3 perovskite. We argued that the two physical characteristics that favor the black perovskite phase at low temperatures are the strong spatial confinement in nanocrystalline structures and the level of electron doping in the material. Within this context, we discussed practical procedures for the realization of long-lasting inorganic lead halide perovskites.

scholarly journals Longevity of Raw and Lyophilized Crude Urease Extracts

2021 ◽  
Vol 2 (2) ◽  
pp. 325-334
Author(s):  
Neda Javadi ◽  
Hamed Khodadadi Tirkolaei ◽  
Nasser Hamdan ◽  
Edward Kavazanjian
Keyword(s):  
Crude Extract ◽  
Room Temperature ◽  
Low Cost ◽  
Extraction Process ◽  
Crude Extracts ◽  
Simple Extraction ◽  
Commercial Applications ◽  
Stable Source ◽  
The Stability ◽  
The Cost

The stability (longevity of activity) of three crude urease extracts was evaluated in a laboratory study as part of an effort to reduce the cost of urease for applications that do not require high purity enzyme. A low-cost, stable source of urease will greatly facilitate engineering applications of urease such as biocementation of soil. Inexpensive crude extracts of urease have been shown to be effective at hydrolyzing urea for carbonate precipitation. However, some studies have suggested that the activity of a crude extract may decrease with time, limiting the potential for its mass production for commercial applications. The stability of crude urease extracts shown to be effective for biocementation was studied. The crude extracts were obtained from jack beans via a simple extraction process, stored at room temperature and at 4 ℃, and periodically tested to evaluate their stability. To facilitate storage and transportation of the extracted enzyme, the longevity of the enzyme following freeze drying (lyophilization) to reduce the crude extract to a powder and subsequent re-hydration into an aqueous solution was evaluated. In an attempt to improve the shelf life of the lyophilized extract, dextran and sucrose were added during lyophilization. The stability of purified commercial urease following rehydration was also investigated. Results of the laboratory tests showed that the lyophilized crude extract maintained its activity during storage more effectively than either the crude extract solution or the rehydrated commercial urease. While incorporating 2% dextran (w/v) prior to lyophilization of the crude extract increased the overall enzymatic activity, it did not enhance the stability of the urease during storage.

scholarly journals A Real-Time Method for Improving Stability of Monolithic Quartz Crystal Microbalance Operating under Harsh Environmental Conditions

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4166
Author(s):  
Román Fernández ◽  
María Calero ◽  
Yolanda Jiménez ◽  
Antonio Arnau
Keyword(s):  
Real Time ◽  
Quartz Crystal Microbalance ◽  
Quartz Crystal ◽  
Limit Of Detection ◽  
Quartz Substrate ◽  
Low Cost ◽  
Discrete Wavelet ◽  
Reagent Consumption ◽  
Measurement Cell ◽  
The Stability

Monolithic quartz crystal microbalance (MQCM) has recently emerged as a very promising technology suitable for biosensing applications. These devices consist of an array of miniaturized QCM sensors integrated within the same quartz substrate capable of detecting multiple target analytes simultaneously. Their relevant benefits include high throughput, low cost per sensor unit, low sample/reagent consumption and fast sensing response. Despite the great potential of MQCM, unwanted environmental factors (e.g., temperature, humidity, vibrations, or pressure) and perturbations intrinsic to the sensor setup (e.g., mechanical stress exerted by the measurement cell or electronic noise of the characterization system) can affect sensor stability, masking the signal of interest and degrading the limit of detection (LoD). Here, we present a method based on the discrete wavelet transform (DWT) to improve the stability of the resonance frequency and dissipation signals in real time. The method takes advantage of the similarity among the noise patterns of the resonators integrated in an MQCM device to mitigate disturbing factors that impact on sensor response. Performance of the method is validated by studying the adsorption of proteins (neutravidin and biotinylated albumin) under external controlled factors (temperature and pressure/flow rate) that simulate unwanted disturbances.

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